The present invention, in some embodiments thereof, relates to creating a labeled dataset for training a Machine Learning (ML) model to identify items offered for sale in a store, and, more specifically, but not exclusively, to creating automatically a labeled dataset for training a supervised ML model to identify items offered for sale in a store based on item identification at the store's checkout Point Of Sale (POS).
Over the past few years, automated and/or autonomous services, platforms and systems have rapidly and dramatically advanced to encompass many applications in the modern era ranging from civil applications (e.g. autonomous cars, custom advertisement, etc.), through industrial, agricultural applications and/or military applications to research and academic work.
Automated stores is a newly introduced member of such automated services and at least partially automated stores have recently opened in the US leading the way to fully automated stores in the future. The automated store aims to allow customers to purchase products in the store without being checked out by a cashier or using a self-checkout station.
To this end the automated stores may employ advanced and complex technologies, including computer vision, machine learning models and algorithms, and sensor fusion to track customers, identify items for sale in the store, and detect interaction of the customers with the items and more in order to automate most of the purchase, checkout, and payment steps associated with a retail transaction.